Decades of beauty: Achieving aesthetic goals throughout the lifespan
Bibliographic record
Abstract
BACKGROUND: Several elements, including age, influence judgments of beauty and attractiveness. Aging is affected by intrinsic factors (e.g., genetics, race/ethnicity, anatomical variations) and extrinsic factors (e.g., lifestyle, environment). AIMS: To provide a general overview of minimally invasive injectable procedures for facial beautification and rejuvenation to meet the aesthetic goals of patients across their lifespan, organized by decade. METHODS: This case study review describes aesthetic considerations of females in their third to sixth decade of life (i.e., 20-60 years of age or beyond). Each case study reports the treatments, specifically botulinum toxin type A and soft tissue fillers, used to address aesthetic concerns. RESULTS: Signs of aging, as well as aesthetic goals and motivations, vary by age groups, cultures, and races/ethnicities. However, there are overarching themes that are associated with each decade of life, such as changes in overall facial shape and specific facial regions, which can be used as a starting point for aesthetic treatment planning. Appropriate patient selection, thorough aesthetic evaluation, product knowledge, and injection technique, as well as good physician-patient communication, are essential for optimal treatment outcomes. CONCLUSIONS: Nonsurgical facial injectable treatments can successfully enhance and rejuvenate facial features across different age ranges. A comprehensive understanding of facial aging and the aesthetic considerations of patients by the decade contributes to optimal treatment planning and maintenance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".